CN107610379A - One kind shopping recognition methods and shopping cart identification device - Google Patents
One kind shopping recognition methods and shopping cart identification device Download PDFInfo
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- CN107610379A CN107610379A CN201710811178.8A CN201710811178A CN107610379A CN 107610379 A CN107610379 A CN 107610379A CN 201710811178 A CN201710811178 A CN 201710811178A CN 107610379 A CN107610379 A CN 107610379A
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Abstract
A kind of shopping recognition methods provided in an embodiment of the present invention and shopping cart identification device, belong to unmanned supermarket's technical field.The bar code information for the commodity that this method is put into shopping cart by obtaining user, so as to generate shopping list, check whether the type and quantity of user's addition shopping cart commodity are consistent with the commodity on the shopping list by the shopping list, and commodity are inconsistent or add the shopping cart after can reminding user in time by commodity barcode scanning when checking commodity not in the shopping list checking, and self-service barcode scanning is carried out to commodity by the shopping cart and identifies commodity that user specifically thrown into the shopping cart and to dutiable value, so as to carry out real-time counting and checkout.Because of the time waited in line needed for commodity barcode scanning when omitting the commodity for not adding shopping list by effectively being avoided to commodity progress image recognition, and efficiently reducing user's checkout, the shopping efficiency of user is improved, saves personal time.
Description
Technical field
The present invention relates to unmanned supermarket's technical field, is identified in particular to one kind shopping recognition methods and shopping cart
Device.
Background technology
Supermarket be people through haunt, be frequently seen customer's queuing checkout before Cash register, and unmanned supermarket can
To reduce cashier, make intelligence checkout and patron self-service cash register into.Current self-service POS and Self-help vending machine are increasingly
Improve and ripe, not only reduce the time that customer waits in line, also improve the shopping efficiency of customer and the cash register of businessman
Efficiency.Life to people brings great convenience.But the self-checkout of current unmanned supermarket, do shopping, pay,
How much is paid all to be determined by customer oneself, just left even if customer does not settle accounts, there will not be staff's obstruction.So according to
Rely customer sincere and the purchase system flow of consciousness, serious threat the interests of businessman, corrupted social values simultaneously.Uniquely
Monitoring device camera also has its limitation, and staff at every moment can not possibly stare at camera, goes observation customer to have
It is to take commodity, either with or without few checkout or even either with or without checkout etc. more, even if staff can have found to fail sincerity in time
The customer of commodity is paid the bill or appropriates public property for personal use, it is in management and more complicated.So as to also exist in the prior art and customer is chosen
The technical problem of identification and the counting of commodity.
The content of the invention
The present invention provides a kind of shopping recognition methods and shopping cart identification device, it is intended to improves above-mentioned technical problem.
A kind of shopping recognition methods provided by the invention, applied to shopping cart identification device, including：Obtain user and be put into purchase
The bar code information of the in-car commodity of thing, and generate shopping list；Judge whether to have in the shopping cart and do not scan bar code
Commodity；If so, obtain the total price of all commodity in the shopping cart.
Preferably, it is described to judge whether there are the commodity for not scanning bar code in the shopping cart, including：Gather the shopping
The image of in-car all commodity；Based on described image, the commodity amount and type of merchandize in the shopping cart are identified；Judge institute
State type of merchandize and whether the commodity amount is consistent with the shopping list；If so, judge that the commodity in the shopping cart are equal
Bar code is scanned；If it is not, judge there are at least one commodity not scan bar code in the shopping cart.
Preferably, the commodity amount and type of merchandize for being based on described image, identifying in the shopping cart, including：Sentence
It is overlapping whether the commodity image in disconnected described image occurs；If so, image point is carried out to described image based on image segmentation algorithm
From obtaining separated target image；Obtain the characteristics of image in the target image；Preset based on described image characteristic query
Image data base, obtain type of merchandize and commodity amount corresponding to described image feature.
Preferably, it is described that image separation is carried out to described image based on image segmentation algorithm, including：Described image is changed
Into gray-scale map；Single stochastic variable based on gray level distributes the intensity level of the pixel of the gray-scale map；Acquisition amplitude is more than 1
Gray level image histogram distribution amplitude threshold, the amplitude threshold meets：Wherein, l0Represent the gray level that amplitude is more than or equal to 1, StRepresent ash
Spend level l0The summation of appearance；The self-adapting window width of each gray level image is obtained based on described image histogram, it is described adaptive
Window width w is answered to meet：Wherein, ltRepresent that amplitude is more than or equal to threshold value in histogram
Amplitude TpGray level sum, α is a constant { α ∈ Z+|1≤α≤lt}；Amplitude is obtained based on vertical scanning histogram to be more than
All peak values of the amplitude threshold；Based on horizontal sweep histogram to obtain true peak gray level, the true peak ash
Spend level lrMeet：Wherein, liExpression belongs to by the window w grey level range r defined gray level
Amplitude；Initiation parameter included in gray level and the gray level image based on peak-peak is clustered by K-means
Algorithm carries out image separation.
Preferably, it is described if so, obtain the total price of all commodity in the shopping cart, also include before：Work as collection
When being reduced to the commodity amount in the shopping cart, judge that user retracts the commodity, and send prompt message.
A kind of shopping cart identification device provided by the invention, including：Data capture unit, shopping is put into for obtaining user
The bar code information of in-car commodity, and generate shopping list；Judging unit, do not swept for judging whether to have in the shopping cart
Retouch the commodity of bar code；Clearing unit, for if so, obtaining the total price of all commodity in the shopping cart.
Preferably, the judging unit includes：Subelement is gathered, for gathering the figure of all commodity in the shopping cart
Picture；Subelement is identified, for based on described image, identifying the commodity amount and type of merchandize in the shopping cart；Judge that son is single
Member, for judging whether the type of merchandize and the commodity amount are consistent with the shopping list；First result unit, is used for
If so, judge that the commodity in the shopping cart have scanned bar code；Second result unit, for if it is not, judging the purchase
Thing in-car has at least one commodity not scan bar code.
Preferably, the identification subelement includes：Judging submodule, for whether judging the commodity image in described image
Occur overlapping；Image procossing submodule, for if so, carrying out image separation, acquisition to described image based on image segmentation algorithm
Separated target image；Acquisition submodule, for obtaining the characteristics of image in the target image；Submodule is inquired about, is used for
Based on described image characteristic query pre-set image database, the type of merchandize and commodity number corresponding to described image feature are obtained
Amount.
Preferably, described image processing submodule is specifically used for：Convert the image into gray-scale map；Based on gray level
Single stochastic variable distributes the intensity level of the pixel of the gray-scale map；The image histogram of gray level of the acquisition amplitude more than 1
The amplitude threshold of distribution, the amplitude threshold meet：Wherein, l0Represent
Amplitude is more than or equal to 1 gray level, StRepresent gray level l0The summation of appearance；Each ash is obtained based on described image histogram
The self-adapting window width of image is spent, the self-adapting window width w meets：Wherein, lt
Represent that amplitude is more than or equal to threshold amplitude T in histogrampGray level sum, α is a constant { α ∈ Z+|1≤α≤
lt}；All peak values of the amplitude more than the amplitude threshold are obtained based on vertical scanning histogram；Based on horizontal sweep histogram with
Obtain true peak gray level, the true peak gray level lrMeet：Wherein, liExpression belongs to
By the amplitude of the window w grey level range r defined gray level；Institute in gray level and the gray level image based on peak-peak
Comprising initiation parameter pass through K-means clustering algorithms carry out image separation.
Preferably, before the clearing unit, in addition to：Computing unit is retracted, is collected for working as in the shopping cart
Commodity amount when reducing, judge that user retracts the commodity, and send prompt message.
A kind of shopping recognition methods and shopping cart identification device that the invention described above provides, shopping is put into by obtaining user
The bar code information of in-car commodity, so as to generate shopping list, check that user adds shopping car trader by the shopping list
Whether the type and quantity of product are consistent with the commodity on the shopping list, and inconsistent or check checking commodity
Commodity can remind user to add the shopping cart after commodity barcode scanning in time not in the shopping list, and pass through institute
Shopping cart is stated to carry out self-service barcode scanning to commodity and identify commodity and correspondence valency that user is specifically thrown into the shopping cart
Lattice, so as to carry out real-time counting and checkout.Shopping is not added by being effectively prevented from omitting to commodity progress image recognition
The commodity of inventory, and the purchase of user is improved because of the time waited in line needed for commodity barcode scanning when efficiently reducing user's checkout
Thing efficiency, save personal time.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below by embodiment it is required use it is attached
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore be not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 is the structured flowchart of electronic equipment provided in an embodiment of the present invention；
Fig. 2 is a kind of flow chart for shopping recognition methods that first embodiment of the invention provides；
Fig. 3 is a kind of flow chart for shopping recognition methods that second embodiment of the invention provides；
Fig. 4 is a kind of high-level schematic functional block diagram for shopping cart identification device that third embodiment of the invention provides.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.Therefore,
The detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit the model of claimed invention below
Enclose, but be merely representative of the selected embodiment of the present invention.Based on the embodiment in the present invention, those of ordinary skill in the art are not having
There is the every other embodiment made and obtained under the premise of creative work, belong to the scope of protection of the invention.
As shown in figure 1, the structured flowchart for a kind of electronic equipment provided in an embodiment of the present invention.The electronic equipment 300
Including shopping cart identification device 400, memory 302, storage control 303, processor 304 and Peripheral Interface 305.
The memory 302, storage control 303, processor 304 and 305 each element of Peripheral Interface are direct between each other
Or be electrically connected with indirectly, to realize the transmission of data or interaction.For example, these elements can pass through one or more between each other
Communication bus or signal wire, which are realized, to be electrically connected with.The shopping cart identification device 400 include it is at least one can be with software or firmware
(firmware) form is stored in the memory 302 or is solidificated in the operating system of the electronic equipment 300
Software function module in (operating system, OS).The processor 304 is used to perform what is stored in memory 302
Executable module, such as the software function module or computer program that the shopping cart identification device 400 includes.
Wherein, memory 302 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only storage (Read Only Memory, ROM), programmable read only memory (Programmable Read-
Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory,
EPROM), electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory,
EEPROM) etc..Wherein, memory 302 is used for storage program, and the processor 304 is after execute instruction is received, described in execution
Program, the method performed by server 100 that the stream process that foregoing any embodiment of the embodiment of the present invention discloses defines can answer
Realized in processor 304, or by processor 304.
Processor 304 is probably a kind of IC chip, has the disposal ability of signal.Above-mentioned processor 304 can
To be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network processing unit
(Network Processor, abbreviation NP) etc.；Can also be digital signal processor (DSP), application specific integrated circuit (ASIC),
Ready-made programmable gate array (FPGA) either other PLDs, discrete gate or transistor logic, discrete hard
Part component.It can realize or perform disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor
Can be microprocessor or any conventional processor etc..
Various input/output devices are coupled to processor 304 and memory 302 by the Peripheral Interface 305.At some
In embodiment, Peripheral Interface 305, processor 304 and storage control 303 can be realized in one single chip.Other one
In a little examples, they can be realized by independent chip respectively.
Referring to Fig. 2, it is a kind of flow chart for shopping recognition methods that first embodiment of the invention provides.The shopping is known
Other method is applied to shopping cart identification device, and the idiographic flow shown in Fig. 2 will be described in detail below.
Step S101, obtains the bar code information for the commodity that user is put into shopping cart, and generates shopping list.
In the present embodiment, the shopping cart is provided with industrial panel computer, barcode scanning module, camera and communication mould
Block.The industrial panel computer is used to show shopping list and handles the purchase data of user.
Wherein, the industrial panel computer includes 8 cun of display screen, J1900 processors, 64G solid state hard discs and 4G internal memories.
The display screen, the solid state hard disc and the internal memory couple with the processor.
Wherein, the scan module can be that one-dimensional two-dimensional high speed scanner that moral is irrigated or other models are swept
Retouch instrument.Here, it is not especially limited.
In the present embodiment, as a kind of application scenarios, the bar of the barcode scanning module acquisition commodity on the shopping cart is passed through
Code information, and accessed bar code information is sent to processor and handled, the processor will be accessed
The bar code information generates shopping list, and is shown by the display screen.
As a kind of embodiment, user passes through selected commodity installed in the shopping cart when being done shopping
On barcode scanning module be scanned, it is raw so as to being handled by the industrial panel computer being pre-installed on the shopping cart
Into shopping list, and show the shopping list.
Step S102, judge whether there are the commodity for not scanning bar code in the shopping cart.
As a kind of embodiment, the commodity in shopping cart are gathered in real time by the camera on the shopping cart
Image, so as to judge whether the commodity in the shopping cart are consistent with the commodity on shopping list.When inconsistent, described in judgement
At least one commodity does not scan bar code in shopping cart.When consistent, judge that the commodity in the shopping cart have been swept
Retouch bar code.Specifically, the image of all commodity in the shopping cart is gathered；Based on described image, the shopping cart is identified
Interior commodity amount and type of merchandize；Judge whether the type of merchandize and the commodity amount are consistent with the shopping list；
If so, judge that the commodity in the shopping cart have scanned bar code；If it is not, judge in the shopping cart have it is at least one
Commodity do not scan bar code.
Wherein, in order to identify the commodity amount and type of merchandize in the shopping cart, it is preferable that first judge in described image
Commodity image whether occur it is overlapping；If so, carrying out image separation to described image based on image segmentation algorithm, obtain through separation
Target image；Obtain the characteristics of image in the target image；Based on described image characteristic query pre-set image database, obtain
Take the type of merchandize and commodity amount corresponding to described image feature.
Wherein, in order to carry out image separation, it is preferable that convert the image into gray-scale map；Based on the single of gray level
Stochastic variable distributes the intensity level of the pixel of the gray-scale map；The image histogram distribution of gray level of the acquisition amplitude more than 1
Amplitude threshold, the amplitude threshold meets： Wherein, l0Represent that amplitude is big
In or equal to 1 gray level, StRepresent gray level l0The summation of appearance；Each gray level image is obtained based on described image histogram
Self-adapting window width, the self-adapting window width w meets：Wherein, ltRepresent
Amplitude is more than or equal to threshold amplitude T in histogrampGray level sum, α is a constant { α ∈ Z+|1≤α≤lt}；It is based on
Vertical scanning histogram obtains all peak values that amplitude is more than the amplitude threshold；Based on horizontal sweep histogram to obtain reality
Peak grayscale level, the true peak gray level lrMeet：Wherein, liExpression belongs to by window w
The amplitude of the grey level range r of definition gray level；Included in gray level and the gray level image based on peak-peak
Initiation parameter carries out image separation by K-means clustering algorithms.
Wherein, in order to convert the image into gray-scale map, it is preferable that in gray level image, use the single of gray level
Stochastic variable distributes the intensity level of pixel.In the case of gray level image, gray level li is defined as { li ∈ Z+∪0|0≤
li≤255}.Each gray level has the appearance grade of its own in gray level image, its be actually histogram functions value or
The density of the gray level.The sum of required block may be calculated：
Wherein,Represent upper limit function；H represents the width of block.In image histogram, the Nogata of 8-bit gray level images
Figure includes 256 blocks, and index value is from 0 to 255.Gray-scale map includes has minimum 0 and the intensity for being up to 255 intensity levels respectively
Level.So the width h of block be chosen as 1 and gray level maximum be 255, then the total k of block be changed into 256.
In the present embodiment, in order to obtain the characteristics of image in the target image, it is preferable that first by principal component point
Analyse (PCA), the gray feature of two kinds of feature extracting method extraction commodity images of linear discriminant analysis (LDA)；Secondly, in order that obtaining
The colour information of commodity image can be utilized, subchannel spy is then carried out to its chrominance component in rgb space and HSI spaces respectively
Sign is extracted and designs three kinds of categorised decision criterions and commodity image is identified；Finally, on the basis using HSI color properties
On, commodity identification is carried out using a kind of secondary classification method.Specifically：
(1), the commodity image feature extraction based on principal component analysis and linear discriminant analysis.
Read in commodity storehouse image.Select in commodity storehouse per a part of image construction training sample set X={ X of class commodityi
(m, n), m=0,1 ..., M-1；N=0,1 ..., N-1 }, each image Xi(m, n) can be using tabular form as M × N one-dimensional vector；
Calculate overall distribution (covariance) matrix.By formulaObtain training sample set
Total population scatter matrix.Wherein K is training sample number, xiFor i-th of training sample vector, μxFor being averaged for all training samples
Vector；
Solve the characteristic value and characteristic vector of scatter matrix；
If commodity image shares c classes, the total sample number for participating in training is N, adheres to this c classification separately.ωiClass has NiIndividual sample
This.Training sample belongs to ωiThe prior probability of class commodity is
By formulaWithGeneration instruction
Practice in the class of sample set, scatter matrix between class；
One group of characteristic vector that most resolution capability is solved using PCA+LDA methods forms transformation matrix.First with
PCA methods obtain the reduced order subspace W of training sample total population scatter matrixPCA, then solve matrixCharacteristic vector form WPCA, obtain final transformation matrixThat is commodity figure
The reduced order subspace of picture；
Extract training sample feature.Only choose characteristic vector composition reduced order subspace corresponding to the preceding d characteristic value of maximum
Td=[a1a2…ad].All training images are projected into the subspace, i.e.,D Wei Te as training sample
Sign vector；
Extract test samples feature.Test samples are equally projected to proper subspace TdOn, it is possible to obtain examining sample
This feature；In the grader that finally the feature input of test samples is trained, result is identified.
In the present embodiment, the target image refers to after carrying out image separation to described image based on image segmentation algorithm
Resulting image.
(2) subchannel feature extraction, is carried out to colored commodity image.
For the training sample set of colored commodity image, first three components of every width coloured image are expressed as arranging
The form of vector, then combines all training images of corresponding same components, forms three respective instructions of component
Practice collection X1, X2, X3；
According to the algorithmic procedure in above-mentioned steps (1), the feature for obtaining three components using PCA or LDA methods respectively is sub
Space W1, W2, W3.By each component of training sample respectively to corresponding eigen-subspace projection, three face of training sample are obtained
The respective characteristic vector of colouring component；
Equally, three color components of each test samples are also projected into each self-corresponding proper subspace, examined
Test the feature of three components of sample；
The design of categorised decision criterion.Average distance method, nearest neighbor classifier is extended in colored commodity image identification,
Nearest neighbor classifier is current relatively direct also more more commonly used sorting technique, and its basic thought is selection and test samples distance
Classification of the classification of nearest training sample as the sample；K nearest neighbor algorithm (K-Nearest Neighbor
Algorithm), three respective recognition results of color component of sample are respectively obtained by nearest neighbor classifier, according to ballot rule
The final affiliation classifier of sample to be identified is therefrom selected for different situations；Centre distance method, calculate sample to be identified and instruction
" centre distance " for practicing each classification in sample obtains the recognition result of each color component, differentiates further according to criterion to be identified
Commodity image generic.
(3), the commodity image identification based on secondary classification method.
Extract the feature of commodity image training sample tri- passages of HSI respectively using PCA or LDA methods, form HSI features
Vector, obtain the color property of training sample.The HSI color properties of same extraction test samples are as commodity original feature；
Using training sample feature, super ellipsoids neutral net is trained, obtained network parameter is as training
Super ellipsoids neural network classifier；
Using training sample feature, error correction SVM (SVMs) grader is trained, obtains error correction svm classifier
The parameter of device；
Sample characteristics are inputted in super ellipsoids neutral net and carry out the first subseries, the sample for result can be identified
This, it is not necessary to the second subseries is carried out, directly using the result as final recognition result；
For the sample for running into rejection in the first subseries or knowing more, then the is carried out again by error correction SVM classifier
Secondary classification, final recognition result is obtained, so as to complete the Classification and Identification to the sample, and extract bar code information.
Wherein, in the present embodiment, in order to obtain the value volume and range of product of commodity, it is preferable that separate and scheme in commodity image
As identification during the commodity image after separation is counted, and according to the bar code that image recognition result is returned from
And obtain type of merchandize and commodity price.
Step S103, if so, obtaining the total price of all commodity in the shopping cart.
When judging that the commodity in the shopping cart are consistent with the commodity on the shopping list, user can be based on described
Shopping list is settled accounts, and can continue to buy commodity.
In the present embodiment, when judging that at least one commodity does not scan bar code in the shopping cart, institute is controlled
The brake for stating shopping cart is braked, so that user can not promote the shopping cart.For example, commodity are identified in shopping cart
During with counting, find to check without such commodity, shopping cart in shopping list.
Referring to Fig. 3, it is the flow chart for the shopping recognition methods that second embodiment of the invention provides.The shopping identification side
Method is applied to shopping cart identification device, and the idiographic flow shown in Fig. 3 will be described in detail below.
Step S201, obtains the bar code information for the commodity that user is put into shopping cart, and generates shopping list.
Step S202, judge whether there are the commodity for not scanning bar code in the shopping cart.
Step S201 and step S202 embodiment refer to step corresponding in first embodiment, here,
Repeat no more.
Step S203, when collecting the reduction of the commodity amount in the shopping cart, judge that user retracts the commodity, and
Send prompt message.
As a kind of embodiment, when the commodity amount is reduced, judge user by the merchandise return arrival of shopping cart
Frame is either put into collection box, i.e. user is retracted the commodity that need not be bought.For example, user directly takes from shopping cart
Go out, i.e., determine whether that commodity take out out of shopping cart by the image collected, for example, being sentenced by judging commodity amount
It is disconnected whether to there are commodity to be taken out easily by the user.Shelf are put back to after swipe shape code, shopping cart prompting subtracts commodity；Another way is to settle accounts
Place is put into collection box after the commodity for being not desired to purchase are taken out into swipe shape code, and shopping cart prompting subtracts commodity.For example, preset when figure
As identification is when taking out commodity and swipe shape code out of shopping cart, to be judged to subtracting commodity.
Step S204, if so, obtaining the total price of all commodity in the shopping cart.
Step S204 embodiment refer to step corresponding in first embodiment, here, repeating no more.
Referring to Fig. 4, it is a kind of high-level schematic functional block diagram for shopping cart identification device that third embodiment of the invention provides.
The shopping cart identification device 400 includes：Data capture unit 410, judging unit 420, retract computing unit 430 and advice of settlement
Member 440.
Data capture unit 410, the bar code information for the commodity being put into for obtaining user in shopping cart, and generate shopping
Inventory.
Judging unit 420, for judging whether there are the commodity for not scanning bar code in the shopping cart.
Wherein, the judging unit 420 includes：Gather subelement, identification subelement, judgment sub-unit, the first result list
Member and the second result unit.
The collection subelement, for gathering the image of all commodity in the shopping cart.
The identification subelement, for based on described image, identifying the commodity amount and type of merchandize in the shopping cart.
Wherein, the identification subelement includes：Judging submodule, image procossing submodule, acquisition submodule and inquiry
Module.
The judging submodule, for judging it is overlapping whether the commodity image in described image occurs.
Described image handles submodule, for if so, based on image segmentation algorithm to described image progress image separation, obtaining
Take separated target image.
Wherein, described image processing submodule is specifically used for：Convert the image into gray-scale map；List based on gray level
Individual stochastic variable distributes the intensity level of the pixel of the gray-scale map；The image histogram point of gray level of the acquisition amplitude more than 1
The amplitude threshold of cloth, the amplitude threshold meet： Wherein, l0Represent amplitude
Gray level more than or equal to 1, StRepresent gray level l0The summation of appearance；Each gray-scale map is obtained based on described image histogram
The self-adapting window width of picture, the self-adapting window width w meet：Wherein, ltRepresent
Amplitude is more than or equal to threshold amplitude T in histogrampGray level sum, α is a constant { α ∈ Z+|1≤α≤lt}；Base
All peak values of the amplitude more than the amplitude threshold are obtained in vertical scanning histogram；Based on horizontal sweep histogram to obtain reality
Border peak grayscale level, the true peak gray level lrMeet：Wherein, liExpression belongs to by window
The amplitude of the gray level for the grey level range r that w is defined；Included in gray level and the gray level image based on peak-peak
Initiation parameter carries out image separation by K-means clustering algorithms.
The acquisition submodule, for obtaining the characteristics of image in the target image.
The inquiry submodule, for based on described image characteristic query pre-set image database, it is special to obtain described image
The corresponding type of merchandize of sign and commodity amount.
The judgment sub-unit, for judge the type of merchandize and the commodity amount whether with the shopping list one
Cause.
The first result unit, for if so, judging that the commodity in the shopping cart have scanned bar code.
The second result unit, for if it is not, judging there are at least one commodity not have scan stripes in the shopping cart
Shape code.
Computing unit 430 is retracted, for when collecting the reduction of the commodity amount in the shopping cart, judging that user retracts
The commodity, and send prompt message.
Clearing unit 440, for if so, obtaining the total price of all commodity in the shopping cart.
In summary, the present invention provides a kind of shopping recognition methods and shopping cart identification device, is put into by obtaining user
The bar code information of commodity in shopping cart, so as to generate shopping list, check that user adds shopping by the shopping list
Whether the type and quantity of car trader's product consistent with the commodity on the shopping list, and check commodity it is inconsistent or inspection
Finding commodity can remind user to add the shopping cart after commodity barcode scanning in time not in the shopping list, and lead to
The shopping cart is crossed to carry out self-service barcode scanning to commodity and identify commodity that user specifically thrown into the shopping cart and right
Dutiable value, so as to carry out real-time counting and checkout.Do not added by efficiently avoid omission to commodity progress image recognition
Because of the time waited in line needed for commodity barcode scanning when entering the commodity of shopping list, and efficiently reducing user's checkout, improve and use
The shopping efficiency at family, save personal time.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, can also pass through
Other modes are realized.Device embodiment described above is only schematical, for example, flow chart and block diagram in accompanying drawing
Show the device of multiple embodiments according to the present invention, method and computer program product architectural framework in the cards,
Function and operation.At this point, each square frame in flow chart or block diagram can represent the one of a module, program segment or code
Part, a part for the module, program segment or code include one or more and are used to realize holding for defined logic function
Row instruction.It should also be noted that at some as in the implementation replaced, the function that is marked in square frame can also with different from
The order marked in accompanying drawing occurs.For example, two continuous square frames can essentially perform substantially in parallel, they are sometimes
It can perform in the opposite order, this is depending on involved function.It is it is also noted that every in block diagram and/or flow chart
The combination of individual square frame and block diagram and/or the square frame in flow chart, function or the special base of action as defined in performing can be used
Realize, or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each functional module in each embodiment of the present invention can integrate to form an independent portion
Point or modules individualism, can also two or more modules be integrated to form an independent part.
If the function is realized in the form of software function module and is used as independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is substantially in other words
The part to be contributed to prior art or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are causing a computer equipment (can be
People's computer, server, or network equipment etc.) perform all or part of step of each embodiment methods described of the present invention.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), arbitrary access
Memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with the medium of store program codes.Need
It is noted that herein, such as first and second or the like relational terms are used merely to an entity or operation
Made a distinction with another entity or operation, and not necessarily require or imply these entities or exist between operating any this
Actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to nonexcludability
Comprising so that process, method, article or equipment including a series of elements not only include those key elements, but also wrapping
Include the other element being not expressly set out, or also include for this process, method, article or equipment intrinsic want
Element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that wanted including described
Other identical element also be present in the process of element, method, article or equipment.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.It should be noted that：Similar label and letter exists
Similar terms is represented in following accompanying drawing, therefore, once being defined in a certain Xiang Yi accompanying drawing, is then not required in subsequent accompanying drawing
It is further defined and explained.
Claims (10)
1. one kind shopping recognition methods, applied to shopping cart identification device, it is characterised in that including：
The bar code information for the commodity that user is put into shopping cart is obtained, and generates shopping list；
Judge whether there are the commodity for not scanning bar code in the shopping cart；
If so, obtain the total price of all commodity in the shopping cart.
2. according to the method for claim 1, it is characterised in that it is described judge whether to have in the shopping cart do not scan bar shaped
The commodity of code, including：
Gather the image of all commodity in the shopping cart；
Based on described image, the commodity amount and type of merchandize in the shopping cart are identified；
Judge whether the type of merchandize and the commodity amount are consistent with the shopping list；
If so, judge that the commodity in the shopping cart have scanned bar code；
If it is not, judge there are at least one commodity not scan bar code in the shopping cart.
3. according to the method for claim 2, it is characterised in that it is described to be based on described image, identify in the shopping cart
Commodity amount and type of merchandize, including：
It is overlapping to judge whether the commodity image in described image occurs；
If so, carrying out image separation to described image based on image segmentation algorithm, separated target image is obtained；
Obtain the characteristics of image in the target image；
Based on described image characteristic query pre-set image database, the type of merchandize corresponding to described image feature and business are obtained
Product quantity.
4. according to the method for claim 3, it is characterised in that described that figure is carried out to described image based on image segmentation algorithm
As separation, including：
Convert the image into gray-scale map；
Single stochastic variable based on gray level distributes the intensity level of the pixel of the gray-scale map；
The amplitude threshold of the image histogram distribution of gray level of the acquisition amplitude more than 1, the amplitude threshold meet：
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Wherein, l0Represent the gray level that amplitude is more than or equal to 1, StRepresent gray level l0The summation of appearance；
The self-adapting window width of each gray level image is obtained based on described image histogram, the self-adapting window width w expires
Foot：
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</mrow>
Wherein, ltRepresent that amplitude is more than or equal to threshold amplitude T in histogrampGray level sum, α is a constant { α ∈
Z+|1≤α≤lt}；
All peak values of the amplitude more than the amplitude threshold are obtained based on vertical scanning histogram；
Based on horizontal sweep histogram to obtain true peak gray level, the true peak gray level lrMeet：
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</mrow>
Wherein, liRepresent the amplitude for belonging to the gray level by the window w grey level range r defined；
Initiation parameter included in gray level and the gray level image based on peak-peak is clustered by K-means to be calculated
Method carries out image separation.
5. according to the method for claim 1, it is characterised in that described if so, obtaining all business in the shopping cart
The total price of product, also includes before：
When collecting the reduction of the commodity amount in the shopping cart, judge that user retracts the commodity, and send prompt message.
A kind of 6. shopping cart identification device, it is characterised in that including：
Data capture unit, the bar code information for the commodity being put into for obtaining user in shopping cart, and generate shopping list；
Judging unit, for judging whether there are the commodity for not scanning bar code in the shopping cart；
Clearing unit, for if so, obtaining the total price of all commodity in the shopping cart.
7. device according to claim 6, it is characterised in that the judging unit includes：
Subelement is gathered, for gathering the image of all commodity in the shopping cart；
Subelement is identified, for based on described image, identifying the commodity amount and type of merchandize in the shopping cart；
Judgment sub-unit, for judging whether the type of merchandize and the commodity amount are consistent with the shopping list；
First result unit, for if so, judging that the commodity in the shopping cart have scanned bar code；
Second result unit, for if it is not, judging there are at least one commodity not scan bar code in the shopping cart.
8. device according to claim 7, it is characterised in that the identification subelement includes：
Judging submodule, for judging it is overlapping whether the commodity image in described image occurs；
Image procossing submodule, for if so, based on image segmentation algorithm to described image progress image separation, obtaining through separation
Target image；
Acquisition submodule, for obtaining the characteristics of image in the target image；
Submodule is inquired about, for based on described image characteristic query pre-set image database, obtaining corresponding to described image feature
Type of merchandize and commodity amount.
9. device according to claim 8, it is characterised in that described image processing submodule is specifically used for：
Convert the image into gray-scale map；
Single stochastic variable based on gray level distributes the intensity level of the pixel of the gray-scale map；
The amplitude threshold of the image histogram distribution of gray level of the acquisition amplitude more than 1, the amplitude threshold meet：
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</mrow>
Wherein, l0Represent the gray level that amplitude is more than or equal to 1, StRepresent gray level l0The summation of appearance；
The self-adapting window width of each gray level image is obtained based on described image histogram, the self-adapting window width w expires
Foot：
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</mrow>
<mn>255</mn>
</msubsup>
<mo>,</mo>
</mrow>
Wherein, ltRepresent that amplitude is more than or equal to threshold amplitude T in histogrampGray level sum, α is a constant { α ∈
Z+|1≤α≤lt}；
All peak values of the amplitude more than the amplitude threshold are obtained based on vertical scanning histogram；
Based on horizontal sweep histogram to obtain true peak gray level, the true peak gray level lrMeet：
<mrow>
<msub>
<mi>l</mi>
<mi>r</mi>
</msub>
<mo>=</mo>
<mrow>
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<msub>
<mi>l</mi>
<mi>i</mi>
</msub>
</msub>
<mo>&Element;</mo>
<mi>r</mi>
<mo>,</mo>
</mrow>
Wherein, liRepresent the amplitude for belonging to the gray level by the window w grey level range r defined；
Initiation parameter included in gray level and the gray level image based on peak-peak is clustered by K-means to be calculated
Method carries out image separation.
10. device according to claim 6, it is characterised in that before the clearing unit, in addition to：
Computing unit is retracted, for when collecting the reduction of the commodity amount in the shopping cart, judging that user retracts the business
Product, and send prompt message.
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| CN201710811178.8A CN107610379A (en) | 2017-09-11 | 2017-09-11 | One kind shopping recognition methods and shopping cart identification device |
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| CN201710811178.8A CN107610379A (en) | 2017-09-11 | 2017-09-11 | One kind shopping recognition methods and shopping cart identification device |
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| CN108520280A (en) * | 2018-04-12 | 2018-09-11 | 河南机电职业学院 | A kind of bulk commodity charge system based on machine vision |
| CN108682091A (en) * | 2018-05-21 | 2018-10-19 | 深圳码隆科技有限公司 | A kind of shopping settlement method, device and user terminal |
| CN108921645A (en) * | 2018-06-07 | 2018-11-30 | 深圳码隆科技有限公司 | A kind of commodity purchasing determination method, device and user terminal |
| CN108921048A (en) * | 2018-06-14 | 2018-11-30 | 深圳码隆科技有限公司 | A kind of shopping settlement method, device and user terminal |
| CN109271920A (en) * | 2018-09-12 | 2019-01-25 | 上海了物网络科技有限公司 | The vision system and method and medium for supporting article to identify |
| CN109353398A (en) * | 2018-09-20 | 2019-02-19 | 北京旷视科技有限公司 | Commodity recognition method, device and system and storage medium and shopping cart |
| CN109977251A (en) * | 2019-03-05 | 2019-07-05 | 武汉摩小超科技有限公司 | A method of building identifies commodity based on RGB histogram feature |
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| CN109977251A (en) * | 2019-03-05 | 2019-07-05 | 武汉摩小超科技有限公司 | A method of building identifies commodity based on RGB histogram feature |
| WO2020187036A1 (en) * | 2019-03-15 | 2020-09-24 | 南京亿猫信息技术有限公司 | Shopping tool-based anti-fraud system |
| CN112116121A (en) * | 2020-07-29 | 2020-12-22 | 大众问问(北京)信息科技有限公司 | Driving route recommendation method and device, electronic device and storage medium |
| CN112215311A (en) * | 2020-09-07 | 2021-01-12 | 上海原能细胞生物低温设备有限公司 | Sample warehousing method and device, computer equipment and storage medium |
| CN112184104A (en) * | 2020-09-18 | 2021-01-05 | 安徽三禾一信息科技有限公司 | Material stacking method for storage |
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Application publication date: 20180119 |